---
title: Ring-2.6-1T
metatags:
    description: "Deploy Ring-2.6-1T with SGLang - a trillion-parameter InclusionAI reasoning model for agent workflows, high/xhigh reasoning effort, and tool use."
---

## 1. Model Introduction

[Ring-2.6-1T](https://huggingface.co/inclusionAI/Ring-2.6-1T) is InclusionAI's trillion-parameter flagship reasoning model for real-world complex task execution. It targets agent workflows, engineering development, scientific research analysis, enterprise automation, and other long-horizon settings where the model must plan, use tools, recover from intermediate errors, and keep context across multiple steps.

**Key Features:**

- **Trillion-Scale Reasoning Model**: `BailingMoeV2_5ForCausalLM` with a `bailing_hybrid` architecture, 80 hidden layers, 256 routed experts, 8 selected experts per token, and FP8 compressed-tensors weights.
- **Agent Execution**: Designed for multi-step task decomposition, tool collaboration, context continuation, and long-horizon execution. The model card reports 87.60 on PinchBench, 63.82 on ClawEval, and 95.32 on Tau2-Bench Telecom for the `high` setting.
- **Reasoning Effort**: The model card describes `high` and `xhigh` reasoning-effort modes. In SGLang's OpenAI-compatible chat API, use top-level `reasoning_effort: "high"` for production agent workflows. To request the model-card `xhigh` prompt path, pass it through `chat_template_kwargs.reasoning_effort`.
- **Hybrid Attention**: Uses the Bailing hybrid stack with MLA plus Lightning linear attention kernels in SGLang.
- **Context Length**: Native 128K in the released config. Configure YaRN separately if you need a 256K deployment.

**Available Models:**

- **FP8 (E4M3 compressed-tensors)**: [inclusionAI/Ring-2.6-1T](https://huggingface.co/inclusionAI/Ring-2.6-1T)

**License:** MIT

## 2. SGLang Installation

Ring-2.6-1T requires recent SGLang builds with Bailing hybrid model support. Start with the latest SGLang Docker image when validating this cookbook:

```bash Command
docker pull lmsysorg/sglang:latest
```

For other installation methods, please refer to the [official SGLang installation guide](../../../docs/get-started/install).

## 3. Model Deployment

Use the selector below to generate a single-node command for the tested hardware targets.

import { Ring261TDeployment } from '/src/snippets/autoregressive/ring-26-1t-deployment.jsx'

<Ring261TDeployment />

### Configuration Tips

- `--trust-remote-code` is required for the model's custom Bailing hybrid implementation.
- Use `--tp-size 4` on a single 4-GPU GB300 node.
- Use `--tp-size 8` on a single 8-GPU B200 node.
- Use `--tp-size 8` on a single 8-GPU H200 node.
- Use `--mem-fraction-static 0.95` on GB300 x4. The model uses about 238.5GB/GPU after loading, so lower values can fail during KV-pool initialization.
- Use `--mem-fraction-static 0.8` on B200 x8.
- Use `--mem-fraction-static 0.95` on H200 x8.
- `--model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}'` is recommended because the model has 175 large safetensors shards.
- Keep `--tool-call-parser glm` enabled by default for OpenAI-compatible tool calls. Ring's template emits XML `<arg_key>/<arg_value>` tool calls, which the `qwen` parser does not convert into `message.tool_calls`.
- Keep `--reasoning-parser deepseek-r1` enabled by default so `<think>...</think>` content is split into `message.reasoning_content`.

## 4. Model Invocation

### 4.1 Basic Usage

For example, launch the server on a single 4-GPU GB300 node:

```bash Command
export PORT=30000

sglang serve \
  --model-path inclusionAI/Ring-2.6-1T \
  --tp-size 4 \
  --trust-remote-code \
  --host 0.0.0.0 \
  --port ${PORT} \
  --mem-fraction-static 0.95 \
  --model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}' \
  --tool-call-parser glm \
  --reasoning-parser deepseek-r1
```

Send a basic chat request:

```bash Command
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "What is the capital of France?"}],
    "max_tokens": 128
  }'
```

### 4.2 Reasoning Effort

Ring-2.6-1T exposes two reasoning-effort levels in the model card: `high` and `xhigh`. In SGLang's OpenAI-compatible chat API, start with top-level `reasoning_effort: "high"` for agent and production workflows:

```bash Command
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "Solve: if 3x + 7 = 52, what is x?"}],
    "reasoning_effort": "high",
    "max_tokens": 512
}'
```

For the model-card `xhigh` path, pass the template value explicitly:

```bash Command
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "Solve: if 3x + 7 = 52, what is x?"}],
    "chat_template_kwargs": {"reasoning_effort": "xhigh"},
    "max_tokens": 512
  }'
```

With the default deployment command, thinking text is separated into `message.reasoning_content` when the model emits `<think>...</think>` blocks.

### 4.3 Tool Calling Example

```bash Command
curl -s http://localhost:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "What is the weather in Beijing?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get the current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {"type": "string", "description": "The city name"}
          },
          "required": ["location"]
        }
      }
    }],
    "tool_choice": "auto",
    "max_tokens": 512
  }'
```

For more API examples, see the [SGLang Basic Usage Guide](../../../docs/basic_usage/send_request).

## 5. Benchmark

### 5.1 Speed Benchmark

- Hardware: NVIDIA B200 GPU (8x), NVIDIA H200 GPU (8x), and NVIDIA GB300 GPU (4x)
- Model: `inclusionAI/Ring-2.6-1T`
- Docker image: `lmsysorg/sglang:latest`
- SGLang version tested: `0.5.11`
- Tensor Parallelism: 8 on B200 x8 and H200 x8, 4 on GB300 x4

Use the deployment command from [Section 3](#3-model-deployment), then confirm that the server is healthy before running benchmarks:

```bash Command
curl -s http://localhost:${PORT}/health
curl -s http://localhost:${PORT}/v1/models
```

#### 5.1.1 Latency-Sensitive Benchmark

- Test Command:

```bash Command
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 \
  --port ${PORT} \
  --model inclusionAI/Ring-2.6-1T \
  --dataset-name random \
  --random-input-len 1024 \
  --random-output-len 1024 \
  --num-prompts 10 \
  --max-concurrency 1 \
  --request-rate inf
```

- Test Results (B200 x8):

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  207.18
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.05
Input token throughput (tok/s):          29.45
Output token throughput (tok/s):         20.37
Total token throughput (tok/s):          49.82
Mean E2E Latency (ms):                   20715.16
Mean TTFT (ms):                          187.86
Mean TPOT (ms):                          44.65
Mean ITL (ms):                           48.76
==================================================
```

- Test Results (GB300 x4):

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  62.21
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.16
Input token throughput (tok/s):          98.07
Output token throughput (tok/s):         67.83
Total token throughput (tok/s):          165.91
Mean E2E Latency (ms):                   6218.57
Mean TTFT (ms):                          233.04
Mean TPOT (ms):                          14.21
Mean ITL (ms):                           14.22
==================================================
```

- Test Results (H200 x8):

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 1
Successful requests:                     10
Benchmark duration (s):                  57.10
Total input tokens:                      6101
Total generated tokens:                  4220
Request throughput (req/s):              0.18
Input token throughput (tok/s):          106.85
Output token throughput (tok/s):         73.91
Total token throughput (tok/s):          180.76
Mean E2E Latency (ms):                   5707.72
Mean TTFT (ms):                          163.35
Mean TPOT (ms):                          13.17
Mean ITL (ms):                           13.17
==================================================
```

#### 5.1.2 Throughput-Sensitive Benchmark

- Test Command:

```bash Command
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 \
  --port ${PORT} \
  --model inclusionAI/Ring-2.6-1T \
  --dataset-name random \
  --random-input-len 1024 \
  --random-output-len 1024 \
  --num-prompts 100 \
  --max-concurrency 100 \
  --request-rate inf
```

- Test Results (B200 x8):

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     100
Benchmark duration (s):                  46.30
Total input tokens:                      50561
Total generated tokens:                  52444
Request throughput (req/s):              2.16
Input token throughput (tok/s):          1092.10
Output token throughput (tok/s):         1132.77
Total token throughput (tok/s):          2224.86
Mean E2E Latency (ms):                   27581.74
Mean TTFT (ms):                          1710.53
Mean TPOT (ms):                          51.27
Mean ITL (ms):                           49.43
==================================================
```

- Test Results (GB300 x4):

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     100
Benchmark duration (s):                  55.80
Total input tokens:                      50561
Total generated tokens:                  52444
Request throughput (req/s):              1.79
Input token throughput (tok/s):          906.10
Output token throughput (tok/s):         939.84
Total token throughput (tok/s):          1845.94
Mean E2E Latency (ms):                   33736.85
Mean TTFT (ms):                          2156.40
Mean TPOT (ms):                          63.09
Mean ITL (ms):                           60.33
==================================================
```

- Test Results (H200 x8):

```text Output
============ Serving Benchmark Result ============
Backend:                                 sglang
Traffic request rate:                    inf
Max request concurrency:                 100
Successful requests:                     100
Benchmark duration (s):                  44.51
Total input tokens:                      50561
Total generated tokens:                  52444
Request throughput (req/s):              2.25
Input token throughput (tok/s):          1135.88
Output token throughput (tok/s):         1178.18
Total token throughput (tok/s):          2314.06
Mean E2E Latency (ms):                   27177.14
Mean TTFT (ms):                          2173.08
Mean TPOT (ms):                          51.11
Mean ITL (ms):                           47.77
==================================================
```

### 5.2 Accuracy Benchmark

#### 5.2.1 GSM8K Benchmark

- Benchmark Command:

```bash Command
python3 -m sglang.test.run_eval \
  --eval-name gsm8k \
  --host 127.0.0.1 \
  --port ${PORT} \
  --model auto \
  --num-examples 200 \
  --num-threads 64 \
  --max-tokens 2048 \
  --reasoning-effort high
```

- Test Results (B200 x8):

```text Output
Total latency: 100.378 s
Score: 0.990
Output throughput: 627.401 token/s
```

- Test Results (GB300 x4):

```text Output
Total latency: 98.386 s
Score: 0.990
Output throughput: 621.469 token/s
```

- Test Results (H200 x8):

```text Output
Total latency: 76.849 s
Score: 0.990
Output throughput: 793.125 token/s
```
